The association between shorter disease course and sarcopenia in women with endometriosis: A retrospective analysis of NHANES 1999-2006

The association between shorter disease course and sarcopenia in women with endometriosis: A retrospective analysis of NHANES 1999-2006 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The association between shorter disease course and sarcopenia in women with endometriosis: A retrospective analysis of NHANES 1999-2006 Litao Sun, Yishu Tian, Lei Ling This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4853579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 May, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Endometriosis is a common gynecological disorder that may cause chronic pelvic pain, infertility, and metabolic disorders. Sarcopenia, a progressive skeletal muscle loss, often affects older adults. This study explored the incidence and risk factors for sarcopenia in endometriosis patients using the NHANES dataset, enrolling 373 participants. Endometriosis was confirmed via self-report questionnaire, while sarcopenia was assessed using dual-energy X-ray absorptiometry. Covariates included age, race, marital status, education level, poverty income ratio, smoking habits, and health conditions. Statistical analyses were conducted using SPSS version 26.0, employing four multivariate regression models. The average age was 40.3 and 40.0 years in endometriotic participants with and without sarcopenia, respectively. Minority ethnicity had higher odds for sarcopenia (OR 6.00, 95% CI 1.24–29.07). Endometriosis duration less than five years was associated with higher sarcopenia risk (OR 4.83, 95% CI 2.57–9.09). Lower educational levels were linked to a reduced chance of developing sarcopenia (OR 0.42, 95% CI 0.21–0.86). These findings were consistent across all regression models, suggesting that endometriosis patients with ethnic minority status, higher educational degrees, and shorter disease duration were more likely to have concurrent sarcopenia. Health sciences/Diseases/Metabolic disorders Health sciences/Diseases/Nutrition disorders Health sciences/Diseases/Reproductive disorders disease course endometriosis (EMT) sarcopenia The National Health and Nutrition Examination Survey (NHANES) Figures Figure 1 Introduction Endometriosis is a chronic gynecological disorder characterized by the presence of endometrial-like tissue outside the uterus 1 . This ectopic endometrial tissue exhibits cyclic hormonal responsiveness, leading to chronic inflammation, pelvic pain, dysmenorrhea, dyspareunia, and infertility 1 . The precise etiology of endometriosis remains elusive, but prevailing theories include retrograde menstruation, coelomic metaplasia, and genetic and immunological factors. Despite affecting approximately 10% of women of reproductive age, endometriosis remains under-researched and underdiagnosed 1 . Advanced research and heightened clinical awareness should be called for timely diagnostic and therapeutic strategies. Long-term complications include chronic pelvic pain, subfertility or infertility, bowel and bladder dysfunction, and some metabolic disorders such as obesity, dyslipidemia, type 2 diabetes, and ovarian cancer 1 , 2 . Sarcopenia is a progressive skeletal muscle disorder characterized by the loss of muscle mass, strength, and function, predominantly affecting older adults 3 – 5 . This condition is multifactorial in origin, involving age-related changes in muscle protein synthesis, chronic inflammation, hormonal alterations, and reduced physical activity. It is increasingly recognized as having metabolic components being associated with metabolic disorders 3 , 4 . Clinically, sarcopenia is associated with adverse outcomes, including physical disability, increased risk of falls and fractures, reduced quality of life, and higher mortality rates 3 , 4 . Diagnosis typically involves assessments of muscle mass, e.g., dual-energy x-ray absorptiometry (DXA), muscle strength (e.g., handgrip strength), and physical performance (e.g., gait speed) 6 . Given its significant disease burden, sarcopenia necessitates comprehensive strategies encompassing early diagnosis, nutritional interventions, resistance training, and pharmacological approaches to mitigate its progression and improve clinical outcomes 3 , 4 , 6 . Despite the two disorders shared identical pathogenesis, no study has assessed the relationship between sarcopenia and endometriosis 2 , 3 . Given the high prevalence of the two disorders and elusive conclusion currently, we aimed to explore the relationship. In this study, data from The National Health and Nutrition Examination Survey (NHANES) 1999–2006 were extracted to investigate the incidence and risk factors for sarcopenia in women with endometriosis. Methods Study design and population The National Health and Nutrition Examination Survey (NHANES) dataset is a nation-wide program to assess the health and nutritional status of adults and children in the United States. It was conducted by the National Center for Health Statistics (NCHS), which is a part of the Centers for Disease Control and Prevention (CDC). NHANES data cover a wide range of health-related items, including participants’ dietary intake, nutritional status, physical activity, medical conditions, and environmental exposures 3 . The cycles of 1999–2000, 2001–2002, 2003–2004, 2005–2006 were only cycles containing endometriotic status of participants in their Reproductive Health Questionnaire (RHQ) 7 . Adult women aged 20 or older with self-reported information on endometriosis (RHQ_D, RHQ360, RHQ370) were included. Those with incomplete information on age, race, height, BMI, poverty income ratio (PIR), marital status, educational level, smoking habits, and a series of health conditions including hypertension, diabetes, cardiovascular diseases, asthma, emphysema or chronic bronchitis, cancer, or Dual-energy x-ray absorptiometry (DXA) data were excluded. As a result, 373 female participants made the finalist. Definition of variables Endometriosis was defined by a positive response to the question “Has a doctor or other health professional ever told you that you had endometriosis?” (RHQ360). Then they were asked about the age of the diagnosis (RHQ370). Sarcopenia was defined as appendicular skeletal muscle mass index (ASMI) ≤ 5.5. ASMI = ASM/height 2 (m 2 ), and appendicular skeletal muscle mass (ASM) is measured by accumulation of the lean mass of the arms and legs assessed by Dual-energy x-ray absorptiometry (DXA) 8 , 9 . The covariates were also collected, including age, race (1 = Mexican American, 2 = Other Hispanic, 3 = Non-Hispanic White, 4 = Non-Hispanic Black, 5 = Other Races), marital status (1 = married or living with partner, 2 = other status), educational level (1 = some college or associates degree, or above, 2 = high school or under), poverty income ratio (≥ 1.0 was defined as rich, while < 1.0 were defined as poor), smoking habits (1 = smoking was defined as smoked more than 20 times in life, SMQ150 between 1999 and 2004 or smoked at least 100 cigarettes in life, SMQ020 from 2005–2006. 2 = non-smoking) 10 . One or more self-reported conditions including hypertension, angina, heart attack, coronary diseases was defined as “Yes” to cardiovascular disease, while “No” if none was reported. Diabetes was also self-reported with “borderline” accounted as disease status. Pulmonary disease was defined with at least one of following disorders: asthma, emphysema or chronic bronchitis. Cancer status was defined as diagnosis of cancer, but the specific types were not further extracted. The duration of the disease course of endometriosis was calculated by participants’ age minus diagnostic age. Long disease duration was defined if the diagnosis was made more than five years ago. Statistical analysis All statistical analyses in this current study were conducted as per Centers for Disease Control and Prevention (CDC) guidelines. We adopted SPSS version 26.0 and an online database ( www.mimicdb.com ) to facilitate variable extraction and statistical analyses. Categorical variables were presented as percentages. Normally distributed continuous variables were presented as mean ± SD, while non-normal distribution was described as normally distributed variables are presented as the median (quartiles 1 and 3). Some continuous variables were transformed into ordinal variables. For example, the BMI was divided into “1” for participants within the normal range. Participants who were underweight were divided into “2”, while BMI ≥ 30 kg/m 2 were “3”. The I 2 test was adopted to compare the categorical variables, whereas the Mann-Whitney U-test was used for continuous parameters. We performed four models to further elucidate the correlation between the disease course and sarcopenia in endometriotic women. We then employed multivariate regression models without any adjustment to generate the crude model (Model 1). In Model 2, we adjusted for participants’ age, race and BMI. In Model 3, apart from covariates in Model 2, marital status (MS), educational level, poverty income ratio (PIR) and smoking status were adjusted. In Model 4, we adjusted for all covariates included in this study: age, race, BMI, MS, education, PIR, smoking habits, cardiovascular disease, pulmonary disease, diabetes and cancer were adjusted. Results Baseline characteristics of included participants The study enrolled 373 patients with endometriosis. The detailed selection process can be seen in Fig. 1 . The average age of the endometriotic patients with and without sarcopenia were 40.3 and 40.0, respectively. Significantly higher risks of sarcopenia were seen in Non-Hispanic Black (29.2% Vs 15.4%) and other races (12.5% Vs 2.8%). Interestingly, patients with higher educational levels were disproportionally higher to be diagnosed with sarcopenia (77.1% Vs 58.8%). The body mass index (BMI) was not contrastingly different between sarcopenic and non-sarcopenic endometriosis patients. Smoking behavior and economic status were similar irrespective of sarcopenia. Additionally, health conditions were also insignificantly different. The detailed demographic information and healthy conditions were demonstrated in Table 1 . Table 1 Baseline characteristics of endometriotic patients with or without sarcopenia Variables Sarcopenia p -value No (N = 325) Yes (N = 48) Age (Mean ± SD) 40.0 ± 8.5 40.3 ± 9.7 0.867 Race* 0.001 Mexican American 27 (8.3%) 3 (6.2%) Other Hispanic 7 (2.2%) 0 (0%) Non-Hispanic White 232 (71.4%) 25 (52.1%) Non-Hispanic Black 50 (15.4%) 14 (29.2%) Other Race 9 (2.8%) 6 (12.5%) BMI Classification (kg/m 2 ) 0.483 Normal range (18.5–24.9) 197 (60.6%) 32 (66.7%) Underweight (< 18.5) 7 (2.2%) 0 (0%) Obese (≥ 30.0) 121 (37.2%) 16 (33.3%) Marital status 0.869 Married 214 (65.8%) 32 (66.7%) Single 111 (34.2%) 16 (33.3%) Educational level* 0.023 College or above 191 (58.8%) 37 (77.1%) High school or under 134 (41.2%) 11 (22.9%) Poverty to income ratio 0.559 Rich (≥ 1.0) 277 (85.2%) 43 (89.6%) Poor (< 1.0) 48 (14.8%) 5 (10.4%) Smoking status 0.673 Yes 43 (13.2%) 8 (16.7%) No 282 (86.8%) 40 (83.3%) Cardiovascular status 0.257 Yes 90 (27.7%) 9 (18.8%) No 235 (72.3%) 39 (81.2%) Diabetic status 0.997 Yes 15 (4.6%) 2 (4.2%) No 310 (95.4%) 46 (95.8%) Pulmonary disease 0.899 Yes 48 (14.8%) 8 (16.7%) No 277 (85.2%) 40 (83.3%) Cancerous status 0.499 Yes 33 (10.2%) 7 (14.6%) No 292 (89.8%) 41 (85.4%) Duration of endometriosis #* 10.0 (5.0–17.0) 4.0 (3.0-11.5) < 0.001 Longer than five years 247 (76%) 19 (39.6%) < 0.001 Less than five years 78 (24%) 29 (60.4%) Univariate analysis The univariate analysis in Table 2 evaluates several factors associated with sarcopenia, highlighting key demographic, socioeconomic, and the impact of health conditions influences. The analysis reveals that race plays a significant role, with individuals from racial minority showing a notably higher odds ratio (OR = 6.00, 95% CI 1.24–29.07, p = 0.026) for sarcopenia compared to Mexican Americans, while non-Hispanic Black individuals also exhibit a higher, though not statistically significant, risk (OR = 2.52, 95% CI 0.67–9.55, p = 0.174). BMI indicates that obese patients were associated with a slightly lower risk of sarcopenia (OR = 0.81, 95% CI 0.43–1.55, p = 0.530) compared to those with a normal BMI. While marital status does not appear to significantly affect the likelihood of sarcopenia, educational level is significantly associated with sarcopenia risk; individuals with a high school education or lower have a reduced odds ratio (OR = 0.42, 95% CI 0.21–0.86, p = 0.018) compared to those with college education or above. The data suggest that educational attainment may increase sarcopenic risks in endometriotic population. Other factors such as smoking status, cardiovascular health, diabetes, pulmonary disease, and cancer status did not show significant associations with sarcopenia in this analysis. Furthermore, the duration of endometriosis diagnosis shows a profound impact, with those diagnosed for less than five years having a significantly higher odds ratio (OR = 4.83, 95% CI 2.57–9.09, p < 0.001) for sarcopenia compared to those diagnosed for more than five years, indicating that shorter duration of the endometriosis is associated with higher sarcopenia risk. These findings underscore the importance of racial background, educational attainment, and disease duration in understanding the risk factors for sarcopenia in endometriotic population. Table 2 Univariate analysis of variables with sarcopenia Variables OR (95% CI) p -value Race Mexican American 1.00 (Reference) Other Hispanic 0.00 (0.00-Inf) 0.987 Non-Hispanic White 0.97 (0.27–3.43) 0.962 Non-Hispanic Black 2.52 (0.67–9.55) 0.174 Other Race 6.00 (1.24–29.07) 0.026 BMI Classification* (kg/m 2 ) Normal range (18.5–24.9) 1.00 (Reference) Underweight (< 18.5) 0.00 (0.00-Inf) 0.987 Obese (≥ 30.0) 0.81 (0.43–1.55) 0.530 Marital status Married 1.00 (Reference) Single 0.96 (0.51–1.83) 0.911 Educational level* College or above 1.00 (Reference) High school or lower 0.42 (0.21–0.86) 0.018 Poverty to income ratio Rich (≥ 1.0) 1.00 (Reference) Poor (< 1.0) 0.67 (0.25–1.78) 0.423 Smoking status No 1.00 (Reference) Yes 1.31 (0.58–2.99) 0.519 Cardiovascular status No 1.00 (Reference) Yes 0.60 (0.28–1.29) 0.194 Diabetic status No 1.00 (Reference) Yes 0.90 (0.20–4.06) 0.889 Pulmonary disease No 1.00 (Reference) Yes 1.15 (0.51–2.62) 0.731 Cancerous status No 1.00 (Reference) Yes 1.51 (0.63–3.64) 0.357 Course of EMT Duration ≥ 5yrs 1.00 (Reference) Duration < 5yrs 4.83 (2.57–9.09) < 0.001 Association between endometriosis and sarcopenia We then assess the association between endometriosis and sarcopenia. The results were showed in Table 3 . In general, a shorter course of endometriosis (recent diagnosis less than five years) was consistent and strongly associated with sarcopenic risks across all models. The original odds ratio (OR) before adjustment was 4.83, with a 95% CI of 2.57 to 9.09, indicating a positive correlation. After adjusting for basic covariates such as age, race, BMI, Model 2 still revealed positive association with an OR at 4.73 (95% CI: 2.48–9.05). Furthermore, the association is particularly pronounced after adjusted for age, race, BMI, marital status, educational level, poverty income ratio and smoking habits (OR: 5.26, 95% CI 2.70-10.25). After adjusted all covariates, the positivity remained with an OR at 5.22 (95% CI 2.67–10.22). Table 3 Association between the course of endometriosis and sarcopenia Crude model Model 2 Model 3 Model 4 Total OR 4.83 (2.57–9.09) 4.73 (2.48, 9.05) 5.26 (2.70, 10.25) 5.22 (2.67–10.22) Model 1: crude model. Model 2: adjust for age, race, BMI Model 3: adjust for age, race, BMI, marital status, education, poverty income ratio, smoking status. Model 4: adjust for age, race, BMI, marital status, education, poverty income ratio, smoking; disease status of hypertension, diabetes, cardiovascular diseases, pulmonary diseases, and cancer. Discussion The study involved 373 endometriosis patients from NHANES dataset 1999–2006 to investigate the link with sarcopenia. The average age of patients with and without sarcopenia was both around 40 years. Notably, racial background and educational level emerged as significant factors; racial minorities and individuals with higher educational attainment faced higher sarcopenia risks. In contrast, BMI, marital status, smoking behavior, and economic status were not significantly associated with sarcopenia risk in endometriotic population. Furthermore, patients diagnosed with endometriosis for less than five years demonstrated a much higher risk of developing sarcopenia, an association that remained robust even after adjusting for various covariates. These results underscore the critical influence of race, education, and disease duration on the risk of sarcopenia in endometriotic patients, suggesting that these factors should be carefully considered in clinical practice. By far, no study has reported the correlation between endometriosis and sarcopenia, but there may be a theoretical overlap in pathogenesis between the two disorders 3 , 4 , 6 . The shared chronic inflammation can be one of the underlying mechanisms 11 , 12 . Endometriosis is characterized by chronic inflammation, demonstrated by elevated pro-inflammatory cytokines, such as TNF-α, interleukin-1 (IL-2), and interleukin-6 (IL-6) 13 , 14 . These cytokines can degrade and inhibit muscle protein synthesis, paving the way to developing sarcopenia. The cytokines’ pro-inflammatory effect further stimulates reactive oxygen species (ROS), perpetuating oxidative stress, which plays a significant role in the development and progression of endometriosis and sarcopenia 15 , 16 . Hormonal imbalance can be another causal factor for concurrent endometriosis and sarcopenia. Abnormally increased estrogen may induce the systemic production of ROS through its metabolism. The production in turn, could cause sarcopenia in some patients 17 , 18 . Lifestyle factors may also add to the shared pathogenesis as endometriosis could cause chronic pelvic pain and reduced physical activity 5 . Long-term inactive lifestyle predisposes individuals to muscle weakness and atrophy, which is a known risk factor for sarcopenia 5 . However, this may not explain our findings that shorter duration of endometriotic diagnosis is more likely to be associated with sarcopenia. The study revealed unexpected results that a shorter course of endometriosis (diagnosis made in less than five years) is positively associated with sarcopenia. This may be explained by several possible hypotheses. One of them suggests a shorter duration may be owing to delayed diagnosis of EMT. As the presentation of endometriosis is similar to many other pelvic disorders, such as irritable bowel syndrome (IBS), chronic appendicitis, pelvic inflammatory disease (PID), fibroids, the diagnosis is often delayed 1 . The occult endometriosis without medical interventions could predispose patients to long-term complications including sarcopenia. From another perspective, a shorter course of later onset endometriosis may imply a disease rapid progression. Although not malignant in nature, endometriotic cells’ biological behavior echoes tumor progression 1 , 11 . The rapid disease progression may add more sarcopenic risks to individuals. Additionally, racial and educational backgrounds also played a contributing role. Further sub-group analysis across different racial contexts could explain the notable diversity observed. Higher educational attainment may be related to more office work and sedentary lifestyle, which caused insufficient muscle training compared physically active individuals. This study has several strengths. Firstly, it is the first research article to report correlation between endometriosis and sarcopenia. The correlation may guide healthcare providers in their daily practice managing endometriotic patients with or without sarcopenia. In time diagnosis and management may reduce disease burden and physical and psychological suffering. Secondly, the correlation between the two health conditions can be extrapolated outside the U.S. As the data were extracted from NHANES, the diverse racial backgrounds enabled reasonable representativeness while extrapolating the conclusion. Thirdly, we proposed four models to demonstrate step-by-step the positive correlation between shorter duration of endometriosis and sarcopenic risks. This association was independent of demographical, lifestyle and other common non-communicable chronic diseases. However, there are some limitations to this study. First, the sample size is small, with only less than 400 participants included. Future research could expand the sample size to better elucidate the association between the two health conditions. Second, racial differences were significant in developing sarcopenia, and was most remarkable in Other Races. Studies with further divided racial, ethnical backgrounds may better guide the medical practice in the future. Third, the endometriosis was self-reported, it could lead to result bias, and deviated association when compared with risks of developing sarcopenia. This was prevalent in NHANES studies as a range of disorders were self-reported instead of diagnosed by laboratory or imaging methods. Conclusion In conclusion, this study suggested that higher educational degree and shorter course of endometriosis were positively associated with a higher risk of sarcopenia. Future prospective cohort study and research experiments may further elucidate the relationship. Declarations Competing Interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (No. 82071929). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Y.T. and L.S. The original draft manuscript was written by Y.T. and L.S. The final manuscript was revised by L.S. and L.L. All authors read and approved the final version of manuscript. Acknowledgement none. Data Availability The datasets used in the manuscript are publicly available, which can be accessed at https://www.cdc.gov/Nchs/Nhanes/. References Giudice, L. C. Clinical practice. Endometriosis. N Engl J Med 362 , 2389-2398, doi:10.1056/NEJMcp1000274 (2010). Barnard, M. E. et al. Endometriosis Typology and Ovarian Cancer Risk. JAMA , doi:10.1001/jama.2024.9210 (2024). Yang, J. et al. The association between the triglyceride-glucose index and sarcopenia: data from the NHANES 2011-2018. Lipids Health Dis 23 , 219, doi:10.1186/s12944-024-02201-1 (2024). Rosenberg, I. H. Sarcopenia: origins and clinical relevance. J Nutr 127 , 990S-991S, doi:10.1093/jn/127.5.990S (1997). Cruz-Jentoft, A. J. & Sayer, A. A. Sarcopenia. Lancet 393 , 2636-2646, doi:10.1016/S0140-6736(19)31138-9 (2019). Yan, K. et al. Higher dietary live microbe intake is associated with a lower risk of sarcopenia. Clin Nutr 43 , 1675-1682, doi:10.1016/j.clnu.2024.05.030 (2024). Hall, M. S., Talge, N. M. & Upson, K. Urinary cadmium and endometriosis prevalence in a US nationally representative sample: results from NHANES 1999-2006. Hum Reprod 38 , 1835-1842, doi:10.1093/humrep/dead117 (2023). Gould, H., Brennan, S. L., Kotowicz, M. A., Nicholson, G. C. & Pasco, J. A. Total and appendicular lean mass reference ranges for Australian men and women: the Geelong osteoporosis study. Calcif Tissue Int 94 , 363-372, doi:10.1007/s00223-013-9830-7 (2014). Cruz-Jentoft, A. J. et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 48 , 16-31, doi:10.1093/ageing/afy169 (2019). Park, S. K., Sack, C., Siren, M. J. & Hu, H. Environmental Cadmium and Mortality from Influenza and Pneumonia in U.S. Adults. Environ Health Perspect 128 , 127004, doi:10.1289/EHP7598 (2020). Saunders, P. T. K. & Horne, A. W. Endometriosis: Etiology, pathobiology, and therapeutic prospects. Cell 184 , 2807-2824, doi:10.1016/j.cell.2021.04.041 (2021). Ferrucci, L. & Fabbri, E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol 15 , 505-522, doi:10.1038/s41569-018-0064-2 (2018). Lv, D., Song, H. & Shi, G. Anti-TNF-alpha treatment for pelvic pain associated with endometriosis. Cochrane Database Syst Rev , CD008088, doi:10.1002/14651858.CD008088.pub2 (2010). Mu, F. et al. A Prospective Study of Inflammatory Markers and Risk of Endometriosis. Am J Epidemiol 187 , 515-522, doi:10.1093/aje/kwx272 (2018). Becker, M. et al. Regulatory T cells require IL6 receptor alpha signaling to control skeletal muscle function and regeneration. Cell Metab 35 , 1736-1751 e1737, doi:10.1016/j.cmet.2023.08.010 (2023). Choi, K. et al. The association of the serum levels of myostatin, follistatin, and interleukin-6 with sarcopenia, and their impacts on survival in patients with hepatocellular carcinoma. Clin Mol Hepatol 26 , 492-505, doi:10.3350/cmh.2020.0005 (2020). Aslam, M. A., Ma, E. B. & Huh, J. Y. Pathophysiology of sarcopenia: Genetic factors and their interplay with environmental factors. Metabolism 149 , 155711, doi:10.1016/j.metabol.2023.155711 (2023). Wiedmer, P. et al. Sarcopenia - Molecular mechanisms and open questions. Ageing Res Rev 65 , 101200, doi:10.1016/j.arr.2020.101200 (2021). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4853579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":346748835,"identity":"fab18f2b-f22b-4f6f-8cfa-e080b70a776d","order_by":0,"name":"Litao Sun","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Litao","middleName":"","lastName":"Sun","suffix":""},{"id":346748836,"identity":"f2440cc4-05b0-455e-b464-9adb1cb1fb89","order_by":1,"name":"Yishu Tian","email":"","orcid":"","institution":"Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yishu","middleName":"","lastName":"Tian","suffix":""},{"id":346748837,"identity":"aa823ae5-5c6c-4421-a5eb-61965bde6fd8","order_by":2,"name":"Lei Ling","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PIQsCMRTA8SeDWaarTxD9CoOD4cfZEEwKxguHXpAziFj1WxiNdwiXptl4YjGezSR6JpNbFNw/DAbvx/YAfL4frEleRwHYofXVuVBhZCe0Igow4MwQUZjcgcCbgN6sh7R1nhEHUm/sryrs1bbpKA91TIHPF8ryseagpwwSkR4HJ71rA5rD1kKYFDpBKrJYnrShIHDkQh7IxB7kWCfEiQSFjhFbCZPgSiSoHAVntI/K5My6C+cmKMtoMk26l+x2D6MOny+/k+oh/Lwx23gVKV2mfD6f7497ApBsQy+I8JrnAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ling","suffix":""}],"badges":[],"createdAt":"2024-08-03 13:39:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4853579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4853579/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-03511-9","type":"published","date":"2025-05-25T15:58:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64191722,"identity":"72e7c234-ee2c-4aaa-a761-b4fbe1fc723e","added_by":"auto","created_at":"2024-09-09 18:46:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82831,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart to demonstrate the selection of participants in this study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4853579/v1/6702ab3a0c4bab27a4a5d12d.jpg"},{"id":83460141,"identity":"7cffc76a-e204-4587-946e-c8a2ff440a26","added_by":"auto","created_at":"2025-05-26 16:10:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":884869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4853579/v1/7a5e9f72-549a-4e73-a023-d1088fdf8ec7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between shorter disease course and sarcopenia in women with endometriosis: A retrospective analysis of NHANES 1999-2006","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis is a chronic gynecological disorder characterized by the presence of endometrial-like tissue outside the uterus\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This ectopic endometrial tissue exhibits cyclic hormonal responsiveness, leading to chronic inflammation, pelvic pain, dysmenorrhea, dyspareunia, and infertility\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The precise etiology of endometriosis remains elusive, but prevailing theories include retrograde menstruation, coelomic metaplasia, and genetic and immunological factors. Despite affecting approximately 10% of women of reproductive age, endometriosis remains under-researched and underdiagnosed\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Advanced research and heightened clinical awareness should be called for timely diagnostic and therapeutic strategies. Long-term complications include chronic pelvic pain, subfertility or infertility, bowel and bladder dysfunction, and some metabolic disorders such as obesity, dyslipidemia, type 2 diabetes, and ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSarcopenia is a progressive skeletal muscle disorder characterized by the loss of muscle mass, strength, and function, predominantly affecting older adults\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This condition is multifactorial in origin, involving age-related changes in muscle protein synthesis, chronic inflammation, hormonal alterations, and reduced physical activity. It is increasingly recognized as having metabolic components being associated with metabolic disorders\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Clinically, sarcopenia is associated with adverse outcomes, including physical disability, increased risk of falls and fractures, reduced quality of life, and higher mortality rates\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Diagnosis typically involves assessments of muscle mass, e.g., dual-energy x-ray absorptiometry (DXA), muscle strength (e.g., handgrip strength), and physical performance (e.g., gait speed)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Given its significant disease burden, sarcopenia necessitates comprehensive strategies encompassing early diagnosis, nutritional interventions, resistance training, and pharmacological approaches to mitigate its progression and improve clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the two disorders shared identical pathogenesis, no study has assessed the relationship between sarcopenia and endometriosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Given the high prevalence of the two disorders and elusive conclusion currently, we aimed to explore the relationship. In this study, data from The National Health and Nutrition Examination Survey (NHANES) 1999\u0026ndash;2006 were extracted to investigate the incidence and risk factors for sarcopenia in women with endometriosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and population\u003c/p\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) dataset is a nation-wide program to assess the health and nutritional status of adults and children in the United States. It was conducted by the National Center for Health Statistics (NCHS), which is a part of the Centers for Disease Control and Prevention (CDC). NHANES data cover a wide range of health-related items, including participants\u0026rsquo; dietary intake, nutritional status, physical activity, medical conditions, and environmental exposures \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe cycles of 1999\u0026ndash;2000, 2001\u0026ndash;2002, 2003\u0026ndash;2004, 2005\u0026ndash;2006 were only cycles containing endometriotic status of participants in their Reproductive Health Questionnaire (RHQ)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Adult women aged 20 or older with self-reported information on endometriosis (RHQ_D, RHQ360, RHQ370) were included. Those with incomplete information on age, race, height, BMI, poverty income ratio (PIR), marital status, educational level, smoking habits, and a series of health conditions including hypertension, diabetes, cardiovascular diseases, asthma, emphysema or chronic bronchitis, cancer, or Dual-energy x-ray absorptiometry (DXA) data were excluded. As a result, 373 female participants made the finalist.\u003c/p\u003e \u003cp\u003eDefinition of variables\u003c/p\u003e \u003cp\u003eEndometriosis was defined by a positive response to the question \u0026ldquo;Has a doctor or other health professional ever told you that you had endometriosis?\u0026rdquo; (RHQ360). Then they were asked about the age of the diagnosis (RHQ370). Sarcopenia was defined as appendicular skeletal muscle mass index (ASMI)\u0026thinsp;\u0026le;\u0026thinsp;5.5. ASMI\u0026thinsp;=\u0026thinsp;ASM/height\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), and appendicular skeletal muscle mass (ASM) is measured by accumulation of the lean mass of the arms and legs assessed by Dual-energy x-ray absorptiometry (DXA)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The covariates were also collected, including age, race (1\u0026thinsp;=\u0026thinsp;Mexican American, 2\u0026thinsp;=\u0026thinsp;Other Hispanic, 3\u0026thinsp;=\u0026thinsp;Non-Hispanic White, 4\u0026thinsp;=\u0026thinsp;Non-Hispanic Black, 5\u0026thinsp;=\u0026thinsp;Other Races), marital status (1\u0026thinsp;=\u0026thinsp;married or living with partner, 2\u0026thinsp;=\u0026thinsp;other status), educational level (1\u0026thinsp;=\u0026thinsp;some college or associates degree, or above, 2\u0026thinsp;=\u0026thinsp;high school or under), poverty income ratio (\u0026ge;\u0026thinsp;1.0 was defined as rich, while\u0026thinsp;\u0026lt;\u0026thinsp;1.0 were defined as poor), smoking habits (1\u0026thinsp;=\u0026thinsp;smoking was defined as smoked more than 20 times in life, SMQ150 between 1999 and 2004 or smoked at least 100 cigarettes in life, SMQ020 from 2005\u0026ndash;2006. 2\u0026thinsp;=\u0026thinsp;non-smoking)\u003csup\u003e10\u003c/sup\u003e. One or more self-reported conditions including hypertension, angina, heart attack, coronary diseases was defined as \u0026ldquo;Yes\u0026rdquo; to cardiovascular disease, while \u0026ldquo;No\u0026rdquo; if none was reported. Diabetes was also self-reported with \u0026ldquo;borderline\u0026rdquo; accounted as disease status. Pulmonary disease was defined with at least one of following disorders: asthma, emphysema or chronic bronchitis. Cancer status was defined as diagnosis of cancer, but the specific types were not further extracted. The duration of the disease course of endometriosis was calculated by participants\u0026rsquo; age minus diagnostic age. Long disease duration was defined if the diagnosis was made more than five years ago.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e All statistical analyses in this current study were conducted as per Centers for Disease Control and Prevention (CDC) guidelines. We adopted SPSS version 26.0 and an online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.mimicdb.com\" target=\"_blank\"\u003ewww.mimicdb.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.mimicdb.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to facilitate variable extraction and statistical analyses. Categorical variables were presented as percentages. Normally distributed continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, while non-normal distribution was described as normally distributed variables are presented as the median (quartiles 1 and 3). Some continuous variables were transformed into ordinal variables. For example, the BMI was divided into \u0026ldquo;1\u0026rdquo; for participants within the normal range. Participants who were underweight were divided into \u0026ldquo;2\u0026rdquo;, while BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e were \u0026ldquo;3\u0026rdquo;. The I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test was adopted to compare the categorical variables, whereas the Mann-Whitney U-test was used for continuous parameters. We performed four models to further elucidate the correlation between the disease course and sarcopenia in endometriotic women. We then employed multivariate regression models without any adjustment to generate the crude model (Model 1). In Model 2, we adjusted for participants\u0026rsquo; age, race and BMI. In Model 3, apart from covariates in Model 2, marital status (MS), educational level, poverty income ratio (PIR) and smoking status were adjusted. In Model 4, we adjusted for all covariates included in this study: age, race, BMI, MS, education, PIR, smoking habits, cardiovascular disease, pulmonary disease, diabetes and cancer were adjusted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics of included participants\u003c/p\u003e \u003cp\u003eThe study enrolled 373 patients with endometriosis. The detailed selection process can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of the endometriotic patients with and without sarcopenia were 40.3 and 40.0, respectively. Significantly higher risks of sarcopenia were seen in Non-Hispanic Black (29.2% Vs 15.4%) and other races (12.5% Vs 2.8%). Interestingly, patients with higher educational levels were disproportionally higher to be diagnosed with sarcopenia (77.1% Vs 58.8%). The body mass index (BMI) was not contrastingly different between sarcopenic and non-sarcopenic endometriosis patients. Smoking behavior and economic status were similar irrespective of sarcopenia. Additionally, health conditions were also insignificantly different. The detailed demographic information and healthy conditions were demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of endometriotic patients with or without sarcopenia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo (N\u0026thinsp;=\u0026thinsp;325)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes (N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Classification\u003c/b\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal range (18.5\u0026ndash;24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (65.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or under\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoverty to income ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich (\u0026ge;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277 (85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (89.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (86.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e235 (72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (81.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310 (95.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (95.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePulmonary disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277 (85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancerous status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292 (89.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of endometriosis\u003c/b\u003e\u003csup\u003e\u003cb\u003e#*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (5.0\u0026ndash;17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0-11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLonger than five years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (39.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than five years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (60.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003cp\u003eThe univariate analysis in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e evaluates several factors associated with sarcopenia, highlighting key demographic, socioeconomic, and the impact of health conditions influences. The analysis reveals that race plays a significant role, with individuals from racial minority showing a notably higher odds ratio (OR\u0026thinsp;=\u0026thinsp;6.00, 95% CI 1.24\u0026ndash;29.07, p\u0026thinsp;=\u0026thinsp;0.026) for sarcopenia compared to Mexican Americans, while non-Hispanic Black individuals also exhibit a higher, though not statistically significant, risk (OR\u0026thinsp;=\u0026thinsp;2.52, 95% CI 0.67\u0026ndash;9.55, p\u0026thinsp;=\u0026thinsp;0.174). BMI indicates that obese patients were associated with a slightly lower risk of sarcopenia (OR\u0026thinsp;=\u0026thinsp;0.81, 95% CI 0.43\u0026ndash;1.55, p\u0026thinsp;=\u0026thinsp;0.530) compared to those with a normal BMI. While marital status does not appear to significantly affect the likelihood of sarcopenia, educational level is significantly associated with sarcopenia risk; individuals with a high school education or lower have a reduced odds ratio (OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI 0.21\u0026ndash;0.86, p\u0026thinsp;=\u0026thinsp;0.018) compared to those with college education or above. The data suggest that educational attainment may increase sarcopenic risks in endometriotic population. Other factors such as smoking status, cardiovascular health, diabetes, pulmonary disease, and cancer status did not show significant associations with sarcopenia in this analysis. Furthermore, the duration of endometriosis diagnosis shows a profound impact, with those diagnosed for less than five years having a significantly higher odds ratio (OR\u0026thinsp;=\u0026thinsp;4.83, 95% CI 2.57\u0026ndash;9.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for sarcopenia compared to those diagnosed for more than five years, indicating that shorter duration of the endometriosis is associated with higher sarcopenia risk. These findings underscore the importance of racial background, educational attainment, and disease duration in understanding the risk factors for sarcopenia in endometriotic population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of variables with sarcopenia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00-Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.27\u0026ndash;3.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.52 (0.67\u0026ndash;9.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00 (1.24\u0026ndash;29.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Classification*\u003c/b\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal range (18.5\u0026ndash;24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00-Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.43\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.51\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.21\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoverty to income ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich (\u0026ge;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.25\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (0.58\u0026ndash;2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.28\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.20\u0026ndash;4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePulmonary disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.51\u0026ndash;2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancerous status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51 (0.63\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCourse of EMT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u0026thinsp;\u0026ge;\u0026thinsp;5yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration\u0026thinsp;\u0026lt;\u0026thinsp;5yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.83 (2.57\u0026ndash;9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociation between endometriosis and sarcopenia\u003c/p\u003e \u003cp\u003eWe then assess the association between endometriosis and sarcopenia. The results were showed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In general, a shorter course of endometriosis (recent diagnosis less than five years) was consistent and strongly associated with sarcopenic risks across all models. The original odds ratio (OR) before adjustment was 4.83, with a 95% CI of 2.57 to 9.09, indicating a positive correlation. After adjusting for basic covariates such as age, race, BMI, Model 2 still revealed positive association with an OR at 4.73 (95% CI: 2.48\u0026ndash;9.05). Furthermore, the association is particularly pronounced after adjusted for age, race, BMI, marital status, educational level, poverty income ratio and smoking habits (OR: 5.26, 95% CI 2.70-10.25). After adjusted all covariates, the positivity remained with an OR at 5.22 (95% CI 2.67\u0026ndash;10.22).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between the course of endometriosis and sarcopenia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.83 (2.57\u0026ndash;9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.73 (2.48, 9.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.26 (2.70, 10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.22 (2.67\u0026ndash;10.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1: crude model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: adjust for age, race, BMI\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3: adjust for age, race, BMI, marital status, education, poverty income ratio, smoking status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 4: adjust for age, race, BMI, marital status, education, poverty income ratio, smoking; disease status of hypertension, diabetes, cardiovascular diseases, pulmonary diseases, and cancer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study involved 373 endometriosis patients from NHANES dataset 1999\u0026ndash;2006 to investigate the link with sarcopenia. The average age of patients with and without sarcopenia was both around 40 years. Notably, racial background and educational level emerged as significant factors; racial minorities and individuals with higher educational attainment faced higher sarcopenia risks. In contrast, BMI, marital status, smoking behavior, and economic status were not significantly associated with sarcopenia risk in endometriotic population. Furthermore, patients diagnosed with endometriosis for less than five years demonstrated a much higher risk of developing sarcopenia, an association that remained robust even after adjusting for various covariates. These results underscore the critical influence of race, education, and disease duration on the risk of sarcopenia in endometriotic patients, suggesting that these factors should be carefully considered in clinical practice.\u003c/p\u003e \u003cp\u003eBy far, no study has reported the correlation between endometriosis and sarcopenia, but there may be a theoretical overlap in pathogenesis between the two disorders\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The shared chronic inflammation can be one of the underlying mechanisms\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Endometriosis is characterized by chronic inflammation, demonstrated by elevated pro-inflammatory cytokines, such as TNF-α, interleukin-1 (IL-2), and interleukin-6 (IL-6)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These cytokines can degrade and inhibit muscle protein synthesis, paving the way to developing sarcopenia. The cytokines\u0026rsquo; pro-inflammatory effect further stimulates reactive oxygen species (ROS), perpetuating oxidative stress, which plays a significant role in the development and progression of endometriosis and sarcopenia\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Hormonal imbalance can be another causal factor for concurrent endometriosis and sarcopenia. Abnormally increased estrogen may induce the systemic production of ROS through its metabolism. The production in turn, could cause sarcopenia in some patients\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Lifestyle factors may also add to the shared pathogenesis as endometriosis could cause chronic pelvic pain and reduced physical activity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Long-term inactive lifestyle predisposes individuals to muscle weakness and atrophy, which is a known risk factor for sarcopenia\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, this may not explain our findings that shorter duration of endometriotic diagnosis is more likely to be associated with sarcopenia. The study revealed unexpected results that a shorter course of endometriosis (diagnosis made in less than five years) is positively associated with sarcopenia. This may be explained by several possible hypotheses. One of them suggests a shorter duration may be owing to delayed diagnosis of EMT. As the presentation of endometriosis is similar to many other pelvic disorders, such as irritable bowel syndrome (IBS), chronic appendicitis, pelvic inflammatory disease (PID), fibroids, the diagnosis is often delayed\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The occult endometriosis without medical interventions could predispose patients to long-term complications including sarcopenia. From another perspective, a shorter course of later onset endometriosis may imply a disease rapid progression. Although not malignant in nature, endometriotic cells\u0026rsquo; biological behavior echoes tumor progression\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The rapid disease progression may add more sarcopenic risks to individuals. Additionally, racial and educational backgrounds also played a contributing role. Further sub-group analysis across different racial contexts could explain the notable diversity observed. Higher educational attainment may be related to more office work and sedentary lifestyle, which caused insufficient muscle training compared physically active individuals.\u003c/p\u003e \u003cp\u003eThis study has several strengths. Firstly, it is the first research article to report correlation between endometriosis and sarcopenia. The correlation may guide healthcare providers in their daily practice managing endometriotic patients with or without sarcopenia. In time diagnosis and management may reduce disease burden and physical and psychological suffering. Secondly, the correlation between the two health conditions can be extrapolated outside the U.S. As the data were extracted from NHANES, the diverse racial backgrounds enabled reasonable representativeness while extrapolating the conclusion. Thirdly, we proposed four models to demonstrate step-by-step the positive correlation between shorter duration of endometriosis and sarcopenic risks. This association was independent of demographical, lifestyle and other common non-communicable chronic diseases. However, there are some limitations to this study. First, the sample size is small, with only less than 400 participants included. Future research could expand the sample size to better elucidate the association between the two health conditions. Second, racial differences were significant in developing sarcopenia, and was most remarkable in Other Races. Studies with further divided racial, ethnical backgrounds may better guide the medical practice in the future. Third, the endometriosis was self-reported, it could lead to result bias, and deviated association when compared with risks of developing sarcopenia. This was prevalent in NHANES studies as a range of disorders were self-reported instead of diagnosed by laboratory or imaging methods.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study suggested that higher educational degree and shorter course of endometriosis were positively associated with a higher risk of sarcopenia. Future prospective cohort study and research experiments may further elucidate the relationship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82071929).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Y.T. and L.S. The original draft manuscript was written by Y.T. and L.S. The final manuscript was revised by L.S. and L.L. All authors read and approved the final version of manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003enone.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used in the manuscript are publicly available, which can be accessed at https://www.cdc.gov/Nchs/Nhanes/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGiudice, L. C. Clinical practice. Endometriosis. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e362\u003c/strong\u003e, 2389-2398, doi:10.1056/NEJMcp1000274 (2010).\u003c/li\u003e\n\u003cli\u003eBarnard, M. E.\u003cem\u003e et al.\u003c/em\u003e Endometriosis Typology and Ovarian Cancer Risk. \u003cem\u003eJAMA\u003c/em\u003e, doi:10.1001/jama.2024.9210 (2024).\u003c/li\u003e\n\u003cli\u003eYang, J.\u003cem\u003e et al.\u003c/em\u003e The association between the triglyceride-glucose index and sarcopenia: data from the NHANES 2011-2018. \u003cem\u003eLipids Health Dis\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 219, doi:10.1186/s12944-024-02201-1 (2024).\u003c/li\u003e\n\u003cli\u003eRosenberg, I. H. Sarcopenia: origins and clinical relevance. \u003cem\u003eJ Nutr\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 990S-991S, doi:10.1093/jn/127.5.990S (1997).\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft, A. J. \u0026amp; Sayer, A. A. Sarcopenia. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e393\u003c/strong\u003e, 2636-2646, doi:10.1016/S0140-6736(19)31138-9 (2019).\u003c/li\u003e\n\u003cli\u003eYan, K.\u003cem\u003e et al.\u003c/em\u003e Higher dietary live microbe intake is associated with a lower risk of sarcopenia. \u003cem\u003eClin Nutr\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 1675-1682, doi:10.1016/j.clnu.2024.05.030 (2024).\u003c/li\u003e\n\u003cli\u003eHall, M. S., Talge, N. M. \u0026amp; Upson, K. Urinary cadmium and endometriosis prevalence in a US nationally representative sample: results from NHANES 1999-2006. \u003cem\u003eHum Reprod\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 1835-1842, doi:10.1093/humrep/dead117 (2023).\u003c/li\u003e\n\u003cli\u003eGould, H., Brennan, S. L., Kotowicz, M. A., Nicholson, G. C. \u0026amp; Pasco, J. A. Total and appendicular lean mass reference ranges for Australian men and women: the Geelong osteoporosis study. \u003cem\u003eCalcif Tissue Int\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 363-372, doi:10.1007/s00223-013-9830-7 (2014).\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft, A. J.\u003cem\u003e et al.\u003c/em\u003e Sarcopenia: revised European consensus on definition and diagnosis. \u003cem\u003eAge Ageing\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 16-31, doi:10.1093/ageing/afy169 (2019).\u003c/li\u003e\n\u003cli\u003ePark, S. K., Sack, C., Siren, M. J. \u0026amp; Hu, H. Environmental Cadmium and Mortality from Influenza and Pneumonia in U.S. Adults. \u003cem\u003eEnviron Health Perspect\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 127004, doi:10.1289/EHP7598 (2020).\u003c/li\u003e\n\u003cli\u003eSaunders, P. T. K. \u0026amp; Horne, A. W. Endometriosis: Etiology, pathobiology, and therapeutic prospects. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e184\u003c/strong\u003e, 2807-2824, doi:10.1016/j.cell.2021.04.041 (2021).\u003c/li\u003e\n\u003cli\u003eFerrucci, L. \u0026amp; Fabbri, E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. \u003cem\u003eNat Rev Cardiol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 505-522, doi:10.1038/s41569-018-0064-2 (2018).\u003c/li\u003e\n\u003cli\u003eLv, D., Song, H. \u0026amp; Shi, G. Anti-TNF-alpha treatment for pelvic pain associated with endometriosis. \u003cem\u003eCochrane Database Syst Rev\u003c/em\u003e, CD008088, doi:10.1002/14651858.CD008088.pub2 (2010).\u003c/li\u003e\n\u003cli\u003eMu, F.\u003cem\u003e et al.\u003c/em\u003e A Prospective Study of Inflammatory Markers and Risk of Endometriosis. \u003cem\u003eAm J Epidemiol\u003c/em\u003e \u003cstrong\u003e187\u003c/strong\u003e, 515-522, doi:10.1093/aje/kwx272 (2018).\u003c/li\u003e\n\u003cli\u003eBecker, M.\u003cem\u003e et al.\u003c/em\u003e Regulatory T cells require IL6 receptor alpha signaling to control skeletal muscle function and regeneration. \u003cem\u003eCell Metab\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 1736-1751 e1737, doi:10.1016/j.cmet.2023.08.010 (2023).\u003c/li\u003e\n\u003cli\u003eChoi, K.\u003cem\u003e et al.\u003c/em\u003e The association of the serum levels of myostatin, follistatin, and interleukin-6 with sarcopenia, and their impacts on survival in patients with hepatocellular carcinoma. \u003cem\u003eClin Mol Hepatol\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 492-505, doi:10.3350/cmh.2020.0005 (2020).\u003c/li\u003e\n\u003cli\u003eAslam, M. A., Ma, E. B. \u0026amp; Huh, J. Y. Pathophysiology of sarcopenia: Genetic factors and their interplay with environmental factors. \u003cem\u003eMetabolism\u003c/em\u003e \u003cstrong\u003e149\u003c/strong\u003e, 155711, doi:10.1016/j.metabol.2023.155711 (2023).\u003c/li\u003e\n\u003cli\u003eWiedmer, P.\u003cem\u003e et al.\u003c/em\u003e Sarcopenia - Molecular mechanisms and open questions. \u003cem\u003eAgeing Res Rev\u003c/em\u003e \u003cstrong\u003e65\u003c/strong\u003e, 101200, doi:10.1016/j.arr.2020.101200 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"disease course, endometriosis (EMT), sarcopenia, The National Health and Nutrition Examination Survey (NHANES)","lastPublishedDoi":"10.21203/rs.3.rs-4853579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4853579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndometriosis is a common gynecological disorder that may cause chronic pelvic pain, infertility, and metabolic disorders. Sarcopenia, a progressive skeletal muscle loss, often affects older adults. This study explored the incidence and risk factors for sarcopenia in endometriosis patients using the NHANES dataset, enrolling 373 participants. Endometriosis was confirmed via self-report questionnaire, while sarcopenia was assessed using dual-energy X-ray absorptiometry. Covariates included age, race, marital status, education level, poverty income ratio, smoking habits, and health conditions. Statistical analyses were conducted using SPSS version 26.0, employing four multivariate regression models. The average age was 40.3 and 40.0 years in endometriotic participants with and without sarcopenia, respectively. Minority ethnicity had higher odds for sarcopenia (OR 6.00, 95% CI 1.24\u0026ndash;29.07). Endometriosis duration less than five years was associated with higher sarcopenia risk (OR 4.83, 95% CI 2.57\u0026ndash;9.09). Lower educational levels were linked to a reduced chance of developing sarcopenia (OR 0.42, 95% CI 0.21\u0026ndash;0.86). These findings were consistent across all regression models, suggesting that endometriosis patients with ethnic minority status, higher educational degrees, and shorter disease duration were more likely to have concurrent sarcopenia.\u003c/p\u003e","manuscriptTitle":"The association between shorter disease course and sarcopenia in women with endometriosis: A retrospective analysis of NHANES 1999-2006","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 18:46:51","doi":"10.21203/rs.3.rs-4853579/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-22T18:00:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T13:40:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T12:47:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225212980846502125449280677676697203655","date":"2025-04-11T02:56:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69916490978487260840999002053574842398","date":"2025-03-28T11:47:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230273762972169320111247573188422430502","date":"2025-03-05T09:55:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211529336432198373414847090238861730717","date":"2024-10-20T19:52:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157646477747687361363498012726732246628","date":"2024-09-03T04:43:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-19T03:40:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-18T01:20:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-08T05:44:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-05T06:49:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-03T13:38:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b8b20fa-ea82-460b-9d6f-79decbbef674","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36771918,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":36771919,"name":"Health sciences/Diseases/Nutrition disorders"},{"id":36771920,"name":"Health sciences/Diseases/Reproductive disorders"}],"tags":[],"updatedAt":"2025-05-26T16:04:37+00:00","versionOfRecord":{"articleIdentity":"rs-4853579","link":"https://doi.org/10.1038/s41598-025-03511-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-25 15:58:29","publishedOnDateReadable":"May 25th, 2025"},"versionCreatedAt":"2024-09-09 18:46:51","video":"","vorDoi":"10.1038/s41598-025-03511-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-03511-9","workflowStages":[]},"version":"v1","identity":"rs-4853579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4853579","identity":"rs-4853579","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}